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From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma
BACKGROUND: Liver hepatocellular carcinoma accounts for the overwhelming majority of primary liver cancers and its belated diagnosis and poor prognosis call for novel biomarkers to be discovered, which, in the era of big data, innovative bioinformatics and computational techniques can prove to be hi...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5354077/ https://www.ncbi.nlm.nih.gov/pubmed/28316892 http://dx.doi.org/10.7717/peerj.3089 |
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author | Yang, Hong Zhang, Xin Cai, Xiao-yong Wen, Dong-yue Ye, Zhi-hua Liang, Liang Zhang, Lu Wang, Han-lin Chen, Gang Feng, Zhen-bo |
author_facet | Yang, Hong Zhang, Xin Cai, Xiao-yong Wen, Dong-yue Ye, Zhi-hua Liang, Liang Zhang, Lu Wang, Han-lin Chen, Gang Feng, Zhen-bo |
author_sort | Yang, Hong |
collection | PubMed |
description | BACKGROUND: Liver hepatocellular carcinoma accounts for the overwhelming majority of primary liver cancers and its belated diagnosis and poor prognosis call for novel biomarkers to be discovered, which, in the era of big data, innovative bioinformatics and computational techniques can prove to be highly helpful in. METHODS: Big data aggregated from The Cancer Genome Atlas and Natural Language Processing were integrated to generate differentially expressed genes. Relevant signaling pathways of differentially expressed genes went through Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes and Panther pathway enrichment analysis and protein-protein interaction network. The pathway ranked high in the enrichment analysis was further investigated, and selected genes with top priority were evaluated and assessed in terms of their diagnostic and prognostic values. RESULTS: A list of 389 genes was generated by overlapping genes from The Cancer Genome Atlas and Natural Language Processing. Three pathways demonstrated top priorities, and the one with specific associations with cancers, ‘pathways in cancer,’ was analyzed with its four highlighted genes, namely, BIRC5, E2F1, CCNE1, and CDKN2A, which were validated using Oncomine. The detection pool composed of the four genes presented satisfactory diagnostic power with an outstanding integrated AUC of 0.990 (95% CI [0.982–0.998], P < 0.001, sensitivity: 96.0%, specificity: 96.5%). BIRC5 (P = 0.021) and CCNE1 (P = 0.027) were associated with poor prognosis, while CDKN2A (P = 0.066) and E2F1 (P = 0.088) demonstrated no statistically significant differences. DISCUSSION: The study illustrates liver hepatocellular carcinoma gene signatures, related pathways and networks from the perspective of big data, featuring the cancer-specific pathway with priority, ‘pathways in cancer.’ The detection pool of the four highlighted genes, namely BIRC5, E2F1, CCNE1 and CDKN2A, should be further investigated given its high evidence level of diagnosis, whereas the prognostic powers of BIRC5 and CCNE1 are equally attractive and worthy of attention. |
format | Online Article Text |
id | pubmed-5354077 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-53540772017-03-17 From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma Yang, Hong Zhang, Xin Cai, Xiao-yong Wen, Dong-yue Ye, Zhi-hua Liang, Liang Zhang, Lu Wang, Han-lin Chen, Gang Feng, Zhen-bo PeerJ Bioinformatics BACKGROUND: Liver hepatocellular carcinoma accounts for the overwhelming majority of primary liver cancers and its belated diagnosis and poor prognosis call for novel biomarkers to be discovered, which, in the era of big data, innovative bioinformatics and computational techniques can prove to be highly helpful in. METHODS: Big data aggregated from The Cancer Genome Atlas and Natural Language Processing were integrated to generate differentially expressed genes. Relevant signaling pathways of differentially expressed genes went through Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes and Panther pathway enrichment analysis and protein-protein interaction network. The pathway ranked high in the enrichment analysis was further investigated, and selected genes with top priority were evaluated and assessed in terms of their diagnostic and prognostic values. RESULTS: A list of 389 genes was generated by overlapping genes from The Cancer Genome Atlas and Natural Language Processing. Three pathways demonstrated top priorities, and the one with specific associations with cancers, ‘pathways in cancer,’ was analyzed with its four highlighted genes, namely, BIRC5, E2F1, CCNE1, and CDKN2A, which were validated using Oncomine. The detection pool composed of the four genes presented satisfactory diagnostic power with an outstanding integrated AUC of 0.990 (95% CI [0.982–0.998], P < 0.001, sensitivity: 96.0%, specificity: 96.5%). BIRC5 (P = 0.021) and CCNE1 (P = 0.027) were associated with poor prognosis, while CDKN2A (P = 0.066) and E2F1 (P = 0.088) demonstrated no statistically significant differences. DISCUSSION: The study illustrates liver hepatocellular carcinoma gene signatures, related pathways and networks from the perspective of big data, featuring the cancer-specific pathway with priority, ‘pathways in cancer.’ The detection pool of the four highlighted genes, namely BIRC5, E2F1, CCNE1 and CDKN2A, should be further investigated given its high evidence level of diagnosis, whereas the prognostic powers of BIRC5 and CCNE1 are equally attractive and worthy of attention. PeerJ Inc. 2017-03-14 /pmc/articles/PMC5354077/ /pubmed/28316892 http://dx.doi.org/10.7717/peerj.3089 Text en ©2017 Yang et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Bioinformatics Yang, Hong Zhang, Xin Cai, Xiao-yong Wen, Dong-yue Ye, Zhi-hua Liang, Liang Zhang, Lu Wang, Han-lin Chen, Gang Feng, Zhen-bo From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma |
title | From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma |
title_full | From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma |
title_fullStr | From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma |
title_full_unstemmed | From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma |
title_short | From big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma |
title_sort | from big data to diagnosis and prognosis: gene expression signatures in liver hepatocellular carcinoma |
topic | Bioinformatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5354077/ https://www.ncbi.nlm.nih.gov/pubmed/28316892 http://dx.doi.org/10.7717/peerj.3089 |
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